System and method for selecting and serving content items based on sensor data from mobile devices

ABSTRACT

Sensor data having values received from several sensors of a mobile device and response data associated with the sensor data may be used in the determination or training of a predictive model. Received sensor data may be input into the predictive model, and the output of the predictive model may be used in the selection and serving of content items to the mobile device. Data to effect presentation of the selected content item may be outputted to the mobile device to effect presentation. In some instances, the predictive model may be updated using the received plurality of values. The updated predictive model may be used in the selection of a subsequent content item for the mobile device. In other implementations, historical sensor data may be used with the set of received sensor data as input for the predictive model.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 13/797,454, titled “SYSTEM AND METHOD FOR SELECTINGAND SERVING CONTENT ITEMS BASED ON SENSOR DATA FROM” filed Mar. 12,2013, of which is hereby incorporated in its entirety.

BACKGROUND

In a networked environment, such as the Internet or other networks,first-party content providers can provide information for publicpresentation on resources, for example web pages, documents,applications, and/or other resources. The first-party content caninclude text, video, and/or audio information provided by thefirst-party content providers via, for example, a resource server forpresentation on a user device over the Internet. Additional third-partycontent can also be provided by third-party content providers forpresentation on the user device together with the first-party contentprovided by the first-party content providers. Thus, a person viewing aresource can access the first-party content that is the subject of theresource, as well as the third-party content that may or may not berelated to the subject matter of the resource.

SUMMARY

One implementation relates to a method that may include receiving afirst content item request and a first set of values that are receivedfrom a respective set of sensors of a mobile device. The set of valuesmay indicate a first ambient condition of the mobile device. A firstcontent item may be selected based, at least in part, on a predictivemodel and the first set of values. Data to effect presentation of theselected first content item may be outputted. Response data associatedwith the selected content item may be received and the predictive modelmay be updated using the received response data. A second content itemrequest and a second set of values may be received from a respective setof sensors of the mobile device. The set of values may indicate a secondambient condition of the mobile device. A second content item may beselected based, at least in part, on the updated predictive model andthe second set of values.

Another implementation relates to a system having a data processor and astorage device storing instructions that, when executed by the dataprocessor, cause the data processor to perform several operations. Theoperations may include receiving a set of historical response dataassociated with a set of content items and a set of historical sensordata associated with the set of historical response data. The operationsmay further include determining a predictive model for the set ofcontent items based, at least in part, on the set of historical responsedata and the set of historical sensor data. The predictive model mayoutput a value for a content item in response to receiving a set ofsensor data from a mobile device. The operations may include selecting acontent item based, at least in part, on an output of the predictivemodel in response to receiving a set of sensor data from a mobiledevice. Data to effect presentation of the selected content item may beoutputted.

Yet a further implementation relates to a computer-readable storagedevice storing instructions that, when executed by one or more dataprocessors, cause the one or more data processors to perform severaloperations. The operations may include receiving a content item request,a set of sensor data, and a device identifier from a mobile device. Theoperations may further include receiving a set of historical sensor dataassociated with the device identifier from a database. The set ofhistorical sensor data may be erased after a pre-determined period oftime. A content item may be selected based, at least in part, on apredictive model. The predictive model may output a value based on thereceived set of sensor data and the received set of historical sensordata. Data to effect presentation of the selected content item with aresource associated with the content item request may be outputted inresponse to the content item request.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages of the disclosure will become apparent from thedescription, the drawings, and the claims, in which:

FIG. 1 is an overview depicting an example system of providinginformation via a computer network;

FIG. 2 is a block diagram of an example mobile device having severalsensors;

FIG. 3 is a block diagram of an example of a system for selectingcontent items using a predictive model;

FIG. 4 is a flow diagram of an example method for aggregating values ofsensor data from one or more mobile devices;

FIG. 5 is a flow diagram of an example method for training a predictivemodel;

FIG. 6 is a flow diagram of an example method for using and updating apredictive model to select a content item;

FIG. 7 is a flow diagram of an example method for using a predictivemodel to select a content item using historical sensor data; and

FIG. 8 is a block diagram illustrating a general architecture for acomputer system that may be employed to implement various elements ofthe systems and methods described and illustrated herein.

It will be recognized that some or all of the figures are schematicrepresentations for purposes of illustration. The figures are providedfor the purpose of illustrating one or more embodiments with theexplicit understanding that they will not be used to limit the scope orthe meaning of the claims.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, methods, apparatuses, and systems forproviding information on a computer network. The various conceptsintroduced above and discussed in greater detail below may beimplemented in any of numerous ways, as the described concepts are notlimited to any particular manner of implementation. Examples of specificimplementations and applications are provided primarily for illustrativepurposes.

A computing device (e.g., a user device) can view a resource, such as aweb page, via the Internet by communicating with a server, such as a webpage server, corresponding to that resource. The resource may includefirst-party content that is the subject of the resource from afirst-party content provider, as well as additional third-party providedcontent, such as advertisements or other content. In one implementation,responsive to receiving a request to access a web page, a web pageserver and/or a user device can communicate with a data processingsystem, such as a content selection system, to request a content item tobe presented with the requested web page. The content selection systemcan select a third-party content item and provide data to effectpresentation of the content item with the requested web page on adisplay of the user device. In some instances, the content item may beselected and served with a resource associated with a search queryresponse. For example, a search engine may return search results on asearch results web page and may include third-party content itemsrelated to the search query in one or more content item slots of thesearch results web page.

The computing device (e.g., a user device) may also be used to view orexecute an application, such as a mobile application. The applicationmay include first-party content that is the subject of the applicationfrom a first-party content provider, as well as additional third-partyprovided content, such as advertisements or other content. In oneimplementation, responsive to use of the application, a web page serverand/or a user device can communicate with a data processing system, suchas a content selection system, to request a content item to be presentedwith the user interface of the application and/or otherwise. The contentselection system can select a third-party content item and provide datato effect presentation of the content item with the application on adisplay of the user device.

In some instances, a device identifier may be associated with the userdevice. The device identifier may be a randomized number associated withthe user device to identify the device during subsequent requests forresources and/or content items. In some instances, the device identifiermay be configured to store and/or cause the user device to transmitinformation related to the user device to the content selection systemand/or resource server (e.g., values of sensor data, a web browser type,an operating system, historical resource requests, historical contentitem requests, etc.).

For situations in which the systems discussed here collect informationabout users, or may make use of information about users, the users maybe provided with an opportunity to control whether programs or featuresthat may collect user information (e.g., information about a user'ssocial network, social actions or activities, a user's preferences, or auser's current location), or to control whether and/or how to receivecontent from the content server that may be more relevant to the user.In addition, certain data may be treated in one or more ways before itis stored or used, so that certain information about the user is removedwhen generating parameters (e.g., demographic parameters). For example,a user's identity may be treated so that no identifying information canbe determined for the user, or a user's geographic location may begeneralized where location information is obtained (such as to a city,ZIP code, or state level), so that a particular location of a usercannot be determined. Thus, users may have control over how informationis collected about them and used by a content server.

A third-party content provider, when providing third-party content itemsfor presentation with requested resources via the Internet or with anapplication, may utilize a content item management account to control orotherwise influence the selection and serving of the third-party contentitems. For instance, a third-party content provider may be able tospecify keywords and corresponding bid values that are used in theselection of the third-party content items. In other implementations,the third-party content provider may be able to specify situations orstates of a user device to be used in the selection and serving ofthird-party content items. In some instances, one or more performancemetrics for the third-party content items may be determined andindications of such performance metrics may be provided to thethird-party content provider via a user interface for the content itemmanagement account. For example, the performance metrics may include acost per impression (CPI) or cost per thousand impressions (CPM), wherean impression may be counted, for example, whenever a content item isselected to be served for presentation with a resource. In someinstances, the performance metric may include a click-through rate(CTR), defined as the number of clicks on the content item divided bythe number of impressions. Still other performance metrics, such as costper action (CPA) (where an action may be clicking on the content item ora link therein, a purchase of a product, a referral of the content item,etc.), conversion rate (CVR), cost per click-through (CPC) (counted whena content item is clicked), cost per sale (CPS), cost per lead (CPL),effective CPM (eCPM), a conversion rate (e.g., a number of sales perimpression), and/or other performance metrics may be used.

In some instances, a third-party content provider may have third-partycontent items that perform better based on a current state of a userdevice. For example, values of sensor data from user devices may becorrelated with clicks or other actions associated with one or morethird-party content items. It should be understood that the values ofsensor data used herein does not include personally-identifiableinformation. Such non-personally-identifiable sensor information mayinclude accelerations, directions of the force of gravity, rotationrates, degrees of rotation, ambient geomagnetic field values, proximityof an object relative to a device, ambient light level, ambienttemperature, ambient humidity, ambient air pressure, ambient acousticlevels, and/or other non-personally-identifiable sensor information ordata.

In one instance, a set of values of sensor data may be indicative of auser device having a series of accelerations and increases in ambientair pressure, such as that which occurs when riding the metro or subway.Response data, such as clicks, associated with the sensor data may beused to determine and/or update one or more predictive models. Thepredictive models may output a value, such as a predicted CTR (pCTR), apredicted CVR (pCVR), etc., for content items based, at least in part,on received sensor data. The value may be used, at least in part, in theselection of a content item to be served to the user device.

The predictive model may be determined based on past sensor data andassociated response data from the user device and/or other user devices.That is, a predictive model may be trained using past sensor data ofuser devices for which a content item was selected and served and, forexample, whether the served content item was clicked or selected. Insome implementations, the predictive model may be determined usingmachine learning algorithms and/or other methods for determining thepredictive model using the sensor data. When sensor data is receivedfrom a user device (e.g., as part of a content item request, associatedwith a content item request, etc.), the sensor data may be used as aninput for the predictive model and the outputted value (e.g., pCTR,pCVR, etc.) for a content item may be used in the selection of a contentitem from several content items. Thus, the sensor data received from auser device may be used in the selection and serving of a content item.

In another example, the past sensor data of a user device may be used inthe selection and serving of a content item. For example, a user maypermit the sensor data of their user device to be temporarily stored fora predetermined period of time (e.g., sensor data for the past minute,past 5 minutes, past 10 minutes, past 30 minutes, past hours, past 2hours, past 4 hours, past 6 hours, past 12 hours, past 24 hours, etc.).The sensor data may be periodically received by a content selectionsystem and/or sent by the user device (such as in response to a requestfrom the content selection system or independently) and temporarilystored in a device state history or otherwise in a database of thecontent selection system. In some instances, the sensor data may bestored chronologically, such as in a time series. Once the predeterminedperiod of time has elapsed, such sensor data from the device statehistory may be deleted, overwritten, and/or otherwise removed from thedatabase such that the sensor data for which the predetermined periodhas elapsed is no longer available.

When a request for a content item is received, the sensor data stored inthe device state history may be utilized in the selection of a contentitem in addition to, or in lieu of, the current sensor data. Forinstance, all the sets of sensor data from the device state historyand/or a subset of the sets of sensor data from the device state historymay be used as an input for the predictive model. By way of example, ifsensor data is stored for the past 5 minutes in 1 minute intervals, the5 sets of sensor data stored in the device state history or otherwise inthe database may be used as input for the predictive model, either as asingle input or a series of inputs. The output value or values of thepredictive model may be used in the selection of a content item, such asselecting content item with the highest output from the predictive modeland/or using outputs from the predictive model as part of anotherselection process (e.g., as part of a content item auction and/orotherwise).

In some implementations, repetitive sensor data (e.g., sensor data thatis similar due to repeated events, such as riding the metro or subway)may be determined from the device state history and utilized as inputfor the predictive model. That is, if values of the sensor data in thedevice state history recurs (such as a series of sets of values ofsensor data having high values for accelerations and ambient airpressure), the recurring sensor data may be utilized as the input forthe predictive model. Thus, the output of the predictive model may beutilized in the selection and serving of a content item for which therecurring sensor data is most relevant. For example, for sets of sensordata that indicate a mobile device is used while jogging, the output ofthe predictive model may be used to select a content item associatedwith jogging, even if the current sensor data does not have valuesindicating the ambient condition of jogging.

In some instances, predictive models may be generated and used for eachthird-party content item. In other instances, predictive models may begenerated for groups or categories of third-party content items (e.g.,content items related to running, movies, etc., sets of content items ofa third-party content provider, and/or otherwise).

While the foregoing has provided an overview of a predictive model inthe selection of content items based on several values for sensor datafrom a user device, more specific examples and systems to implement sucha predictive model will now be described herein.

FIG. 1 is a block diagram of an example system 100 for providinginformation via at least one computer network such as the network 106.The network 106 can include computer networks such as the Internet,local, wide, metro or other area networks, intranets, and other computernetworks such as voice or data mobile phone communication networks. Thesystem 100 can also include at least one data processing system, such asa content selection system 108. The content selection system 108 caninclude at least one logic device such as a computing device having adata processor to communicate via the network 106, for example with aresource server 104, a user device 110, and/or a third-party contentserver 102. The content selection system 108 can include one or moredata processors, such as a content placement processor configured toprocess information and provide data to effect presentation of one ormore content items to the resource server 104 or the user device 110,and one or more databases configured to store data. The contentselection system 108 can include a server, such as an advertisementserver or otherwise.

The user device 110 can include one or more mobile computing devicessuch as a laptop, smart phone, tablet, and/or personal digital assistantdevice configured to communicate with other devices via the network 106.The computing device may be any form of portable electronic device thatincludes a data processor and a memory, i.e., a processing module. Theprocessor may include a microprocessor, an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), etc.,or combinations thereof. The memory may store machine instructions that,when executed by a processor, cause the processor to perform one or moreof the operations described herein. The memory may also store data toeffect presentation of one or more resources, content items, etc. on thecomputing device. The memory may include, but is not limited to,electronic, optical, magnetic, or any other storage or transmissiondevice capable of providing processor with program instructions. Thememory may include a floppy disk, compact disc read-only memory(CD-ROM), digital versatile disc (DVD), magnetic disk, solid statedrive, memory chip, read-only memory (ROM), random-access memory (RAM),Electrically Erasable Programmable Read-Only Memory (EEPROM), erasableprogrammable read only memory (EPROM), flash memory, optical media, orany other suitable memory from which processor can read instructions.The instructions may include code from any suitable computer programminglanguage such as, but not limited to, C, C++, C#, Java®, ActionScript®,JavaScript®, JSON, Perl®, HTML, HTML5, XML, Python®, and Visual Basic®.The user device 110 can execute a software application (e.g., a webbrowser or other application) to retrieve content from other computingdevices over network 106.

The resource server 104 can include a computing device, such as aserver, configured to host a resource, such as a web page or otherresource (e.g., articles, comment threads, music, video, graphics,search results, information feeds, etc.). The resource server 104 may bea computer server (e.g., a file transfer protocol (FTP) server, filesharing server, web server, etc.) or a combination of servers (e.g., adata center, a cloud computing platform, etc.). The resource server 104can provide resource data or other content (e.g., text documents, PDFfiles, application data, and other forms of electronic documents ordata) to the user device 110. In one implementation, the user device 110can access the resource server 104 via the network 106 to request datato effect presentation of a resource of the resource server 104.

One or more third-party content providers may have third-party contentservers 102 to directly, indirectly, or otherwise provide data forthird-party content items to the content selection system 108 and/or toother computing devices via network 106. The content items may be in anyformat that may be presented on a display of a user device 110, forexample, graphical, text, image, audio, video, etc. The content itemsmay also be a combination (hybrid) of the formats. The content items maybe banner content items, interstitial content items, pop-up contentitems, rich media content items, hybrid content items, etc. The contentitems may also include embedded information such as hyperlinks,metadata, links, machine-executable instructions, annotations, etc. Insome instances, the third-party content servers 102 may be integratedinto the content selection system 108 and/or the data for thethird-party content items may be stored in a database of the contentselection system 108.

In one implementation, the content selection system 108 can receive, viathe network 106, a request for a content item to present with a resourceand/or an application (such as a mobile application). The receivedrequest may be received from a resource server 104, a user device 110(e.g., a mobile device executing a mobile application, etc.), and/or anyother computing device. The resource server 104 may be owned or ran by afirst-party content provider that may have an agreement with the ownerof the content selection system 108 for the system to providethird-party content items to present with one or more resources of thefirst-party content provider on the resource server 104. In oneimplementation, the resource may include a web page. In another example,an application may be developed by a first-party content provider thatmay have an agreement with the owner of the content selection system 108for the system to provide third-party content items to present with theapplication of the first-party content provider.

The user device 110 may be a computing device operated by a user(represented by a device identifier), which, when accessing a resourceof the resource server 104 and/or executing an application, can make arequest to the content selection system 108 for content items to bepresented with the resource and/or application, for instance. Thecontent item request can include requesting device information (e.g., aweb browser type, an operating system type, one or more previousresource requests from the requesting device, one or more previouscontent items received by the requesting device, a language setting forthe requesting device, a time of a day at the requesting device, a dayof a week at the requesting device, a day of a month at the requestingdevice, a day of a year at the requesting device, etc.) and resourceand/or application information (e.g., a uniform resource locator (URL)of the requested resource, one or more keywords of the content of therequested resource, text of the content of the resource, a title of theresource, one or more keywords of the content of the application, textof the content of the application, a title of the application, etc.).The device information that the content selection system 108 receivescan include a HyperText Transfer Protocol (HTTP) cookie which contains adevice identifier (e.g., a random number, alphanumeric string, etc.)that represents the user device 110.

In some instances, sensor data from the user device 110 may also beincluded, either as part of the content item request and/or separately.For example, a resource from the resource server 104 may include coding,such as HTML5, JavaScript®, etc., that requests sensor data from one ormore sensors of the user device and returns the sensor data to thecontent selection system 108. In some instances, the sensor data may beincorporated into the content item request with the device informationand/or resource information (such as appending a string of encodedsensor data to a URL request) or the sensor data may be transmitted tothe content selection system 108 separately. The requesting deviceinformation, the resource information, and/or the sensor data may beutilized by the content selection system 108 to select third-partycontent items to be served with the requested resource and presented ona display of a user device.

A resource may include a search engine feature. The search enginefeature may receive a search query (e.g., a string of text) via an inputfeature (an input text box, etc.). The search engine may search an indexof documents (e.g., other resources, such as web pages, etc.) forrelevant search results based on the search query. The search resultsmay be transmitted as a second resource to present the relevant searchresults, such as a search result web page, on a display of a user device110. The search results may include web page titles, hyperlinks, etc.One or more third-party content items may also be presented with thesearch results in a content item slot of the search result web page.Accordingly, the resource server 104 and/or the user device 110 mayrequest one or more content items from the content selection system 108to be presented in the content item slot of the search result web page.The content item request may include additional information, such as aquantity of content items, a format for the content items, the searchquery string, keywords of the search query string, information relatedto the query (e.g., temporal information), etc. In some implementations,a delineation may be made between the search results and the third-partycontent items to avert confusion. The resource server 104 and/or userdevice 110 may transmit information regarding the selected content itemto the content selection system 108, such as data indicating whether thecontent item was clicked on.

In some implementations, the third-party content provider may manage theselection and serving of content items by content selection system 108.For example, the third-party content provider may set selection criteriavia a user interface that may include one or more content itemconditions or constraints regarding the serving of content items. Athird-party content provider may specify that a content item and/or aset of content items should be selected and served for user devices 110having device identifiers associated with a certain language, a certainoperating system, a certain web browser, etc. In another example, thethird-party content provider may specify that a content item or set ofcontent items should be selected and served when the resource, such as aweb page, document, etc., contains content that matches or is related tocertain keywords, phrases, etc. In other instances, the third-partycontent provider may specify that a content item or set of content itemsshould be selected and served when particular sets of values for sensordata is received. Of course the third-party content provider may includeother selection criteria as well.

FIG. 2 is a block diagram of an example mobile device 200, which may bea user device 110 of FIG. 1, having several sensors, a processor 230, adata storage device 232, a display 234, input/output devices 236, and anetwork transceiver 238. The mobile device 200 may include a smartphone,a cellular telephone, a tablet, a laptop, a portable media device, awearable display or glasses, and/or any other portable electronicdevice. The mobile device 200 may include a bus or other communicationcomponent for communicating information between the various componentsof the mobile device 200. The processor 230 or processor may be coupledto the bus and/or otherwise for processing information, data, and/orinstructions from one or more components of the mobile device 200. Thedata storage device 232 may include dynamic storage devices, such as aRAM or other dynamic storage devices, and may be coupled to the busand/or otherwise for storing information, data, and/or instructions tobe executed by the processor 230. The data storage device 232 can alsobe used for storing position information, temporary variables, and/orother intermediate information during execution of instructions by theprocessor 230. In some instances, the data storage device 232 mayinclude a static storage device, in addition to or instead of thedynamic storage device, such as ROM, solid state drive (SSD), flashmemory (e.g., EEPROM, EPROM, etc.), magnetic disc, optical disc, etc.,that is coupled to the bus and/or otherwise for storing staticinformation, data, and/or instructions for the processor 230.

The display 234 may be coupled via the bus and/or otherwise to othercomponents of the mobile device 200. The display may include a LiquidCrystal Display (LCD), Thin-Film-Transistor LCD (TFT), an Organic LightEmitting Diode (OLED) display, LED display, Electronic Paper display,Plasma Display Panel (PDP), and/or other display, etc., for displayinginformation to a user. The input/output devices 236 may include devicessuch as a keyboard having alphanumeric and other keys, a microphone, aspeaker, an LED, a trackball, a mouse, cursor direction keys, etc. thatmay be coupled to the bus for communicating information and/or commandselections to the processor 230. In another implementation, an inputdevice may be integrated with the display 234, such as in a touch screendisplay.

The network transceiver 238 may be configured to interface with anetwork, such as network 106 of FIG. 1. Example of such a network mayinclude the Internet, local, wide, metro or other area networks,intranets, voice or data mobile device communication networks, and/orother networks. In one example, the network transceiver 238 may includevoice and/or data mobile device communication network through which themobile device 200 may communicate with remote devices, such asthird-party content server 120, resource server 104, and/or contentselection system 108 of FIG. 1. The voice and/or data mobile devicecommunication network may include those operating under the standards ofGSM, EDGE, GPRS, CDMA, UMTS, WCDMA, HSPA, HSPA+, LTE, and/or othermobile device network standards. In some implementations, the networktransceiver 238 may include other network transceivers, such as a WiFi™transceiver, a Bluetooth® transceiver, a cellular transceiver, an NFCtransceiver, etc. In still further implementations, the mobile device200 may include several network transceivers 238, such that the mobiledevice 200 may communicate with several networks. Of course, othernetwork transceivers 238 for the mobile device 200 to communicate withremote devices may be used as well.

According to various implementations, the processor 230 may execute anarrangement of instructions contained in the data storage device 232.Such instructions can be read into the data storage device 232 fromanother computer-readable medium. Execution of the arrangement ofinstructions contained in the data storage device 232 may cause themobile device 200 to perform various operations, such as reading dataoutput from one or more sensors, transmitting data to a remote devicevia the network transceiver 238, displaying information on the display234, and/or otherwise. One or more processors in a multi-processingarrangement may also be employed to execute the instructions containedin the data storage device 232. In alternative implementations,hard-wired circuitry may be used in place of or in combination withsoftware instructions to effect the various operations. Thus,implementations are not limited to any specific combination of hardwarecircuitry and software. Of course, other configurations or componentsfor mobile device 200 may be included.

The several sensors may include a gyroscope 210, an accelerometer 212, amagnetometer 214, a barometer 216, a proximity sensor 218, a temperaturesensor 220, a light sensor 222, a humidity sensor 224, an acousticsensor 226, and/or other sensors 228. Of course mobile device 200 mayhave other features, components, etc. as well.

In the present example, gyroscope 210 may be communicatively coupled tothe processor 230, the data storage device 232, the display 234, theinput/output devices 236, the network transceiver 238, and/or othercomponents of the mobile device 200. The gyroscope 210 may include amicroelectromechanical system (MEMS) gyroscope, a vibrating structuregyroscope (VSG) (e.g., a tuning fork gyroscope), and/or any othergyroscope. The gyroscope 210 may be configured measure the rate ofrotation of the mobile device 200 about one, two, and/or three axesand/or the degrees of rotation relative to one, two, and/or three axes.The values of the rates of rotation and/or the values of the degrees ofrotation may be output to the processor 230, the data storage device232, the network transceiver 238, and/or any other component of themobile device 200.

The accelerometer 212 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The accelerometer 212 may include a MEMSaccelerometer, a piezoelectric accelerometer, and/or any otheraccelerometer. The accelerometer 212 may be configured measure theacceleration of the mobile device 200 in one, two, and/or three axesand/or the direction of gravity relative to one, two, and/or three axes.The values of the accelerations and/or the values of the force ofgravity for the one, two, and/or three axes may be output to theprocessor 230, the data storage device 232, the network transceiver 238,and/or any other component of the mobile device 200.

The magnetometer 214 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The magnetometer 214 may include a magnetoresistivesensor and/or any other magnetometer. The magnetometer 214 may beconfigured measure the ambient geomagnetic field relative to the mobiledevice 200 in one, two, and/or three axes. The values of the ambientgeomagnetic fields for the one, two, and/or three axes may be output tothe processor 230, the data storage device 232, the network transceiver238, and/or any other component of mobile device 200.

The barometer 216 may be communicatively coupled to the processor 230,the data storage device 232, the display 234, the input/output devices236, the network transceiver 238, and/or other components of the mobiledevice 200. The barometer 216 may include a digital barometer, ananeroid barometer, and/or any other barometer. The barometer 216 may beconfigured measure the air pressure around the mobile device 200. Thevalue of the air pressure may be output to the processor 230, the datastorage device 232, the network transceiver 238, and/or any othercomponent of mobile device 200.

The proximity sensor 218 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The proximity sensor 218 may include a reflectiveproximity sensor, an ultrasonic proximity sensor, and/or any otherproximity sensor. The proximity sensor 218 may be configured measure theproximity of an object relative to the mobile device 200. The value ofthe proximity of the object may be output to the processor 230, the datastorage device 232, the network transceiver 238, and/or any othercomponent of mobile device 200.

The temperature sensor 220 may be communicatively coupled to theprocessor 230, the data storage device 232, the display 234, theinput/output devices 236, the network transceiver 238, and/or othercomponents of the mobile device 200. The temperature sensor 220 mayinclude a thermistor, a thermocouple, a silicon bandgap temperaturesensor, an infrared thermometer, and/or any other temperature sensor.The temperature sensor 220 may be configured measure the ambienttemperature around the mobile device 200. The value of the temperaturemay be output to the processor 230, the data storage device 232, thenetwork transceiver 238, and/or any other component of mobile device200.

The light sensor 222 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The light sensor 222 may include a charge-coupleddevice (CCD), a photodiode, a photovoltaic cell, and/or any otherphotodetector or light sensor. The light sensor 222 may be configuredmeasure the ambient light level around the mobile device 200. The valueof the light level may be output to the processor 230, the data storagedevice 232, the network transceiver 238, and/or any other component ofmobile device 200.

The humidity sensor 224 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The humidity sensor 224 may include a humistor, acapacitive humidity sensor, a thermal conductivity humidity sensor,and/or any other humidity sensor. The humidity sensor 224 may beconfigured measure the ambient humidity level around the mobile device200. The value of the humidity level may be output to the processor 230,the data storage device 232, the network transceiver 238, and/or anyother component of mobile device 200.

The acoustic sensor 226 may be communicatively coupled to the processor230, the data storage device 232, the display 234, the input/outputdevices 236, the network transceiver 238, and/or other components of themobile device 200. The acoustic sensor 226 may include a MEMSmicrophone, a fiber optic microphone, and/or any other acoustic sensor.The acoustic sensor 226 may be configured measure the ambient acousticlevel around the mobile device 200. The value of the acoustic level maybe output to the processor 230, the data storage device 232, the networktransceiver 238, and/or any other component of mobile device 200.

While the foregoing has describes some examples of sensors that may beincluded in a mobile device 200, still other sensors 228 may be includedas well, such as atmospheric sensors, fluid velocity sensors, forcesensors, etc.

In some implementations, the values from the several sensors may be readand transmitted via the network transceiver 238 in response to receivingdata via the network transceiver 238. For example, a mobile device 200may request a resource, such as a mobile webpage, by sending the requestfor data for the resource to a resource server, such as resource server104, via the network transceiver 238. The resource server may receivethe request and transmit data to effect presentation of the resource onthe display 234 of the mobile device 200. The data for the resource mayinclude one or more content item slots for displaying one or morethird-party content items when the resource is presented for display bythe mobile device 200. In some implementations, the data for therequested resource may include code or piece of code, such as aJavaScript® script, that may cause the processor 230 of the mobiledevice 200 to read one or more values from one or more sensors of themobile device 200 and transmit the one or more values to a remotedevice, such as content selection system 108, via the networktransceiver 238. By way of example, the one or more values from the oneor more sensors of the mobile device 200 may be appended to a contentitem request URL (e.g.,http://contentitem.item/page/contentitem?party=abc123&sensor1=5&sensor2=7&sensor3=45orhttp://contentitem.item/page/contentitem?party=abc123&devicestate=98765).Thus, one or more of the several sensors of the mobile device 200 mayhave the current values read and transmitted to a content selectionsystem. Such values may be used by the content selection system toselect a content item to be served with the requested resource. In someinstances, the code may repeatedly cause the processor 230 of the mobiledevice 200 to read and transmit the values of one or more sensors toselect and serve several third-party content items. The code may includeinstructions to read and transmit the values after a predeterminedperiod of time (e.g., every 30 seconds, every 1 minute, every 2 minutes,every 5 minutes, etc.), after the occurrence of an event, and/or ondemand.

In another implementation, the values from the several sensors may beread and transmitted via the network transceiver 238 in response toexecuting a mobile application on the mobile device 200. For example, amobile application may include a content item slot for presentingthird-party content items while the mobile application is beingexecuted. The mobile application may include instructions that cause theprocessor 230 of the mobile device 200 to read the values of one or moresensors and transmit the values of the one or more sensors to a remotedevice, such as content selection system 108, via the networktransceiver 238. By way of example, the one or more values from the oneor more sensors of the mobile device 200 may be appended to a contentitem request URL (e.g.,http://contentitem.item/page/contentitem?party=abc123&sensor1=5&sensor2=7&sensor3=45orhttp://contentitem.item/page/contentitem?party=abc123&devicestate=98765).Such values may be used by the content selection system to select acontent item to be served while the mobile application is executing onthe mobile device 200. In some instances, the mobile application mayrepeatedly cause the processor 230 of the mobile device 200 to read andtransmit the values of one or more sensors to select and serve severalthird-party content items. The mobile application may includeinstructions to read and transmit the values after a predeterminedperiod of time (e.g., every 30 seconds, every 1 minute, every 2 minutes,every 5 minutes, etc.), after the occurrence of an event, and/or ondemand.

In other instances, the values from the several sensors may be read andstored in the data storage device 232. The values may be individuallystored or stored as a set, such as in a log for a device state. Thestored values or set of values may be transmitted to a remote device viathe network transceiver 238. When a content item request is to betransmitted, such as in response to executing code of a mobile webpageand/or in response to instructions from an executed mobile application,the stored set of values may be transmitted via the network transceiver238 to a content selection system, such as content selection system 108of FIG. 1, as part of or with a content item request sent to the contentselection system. The stored values or set of values of the one or moresensors may be periodically read, stored, and/or transmitted forsuccessive predetermined periods of time, such as for subsequent contentitem requests.

In another example, several sets of values may be stored in the datastorage device 232 and may all be transmitted, or a subset may betransmitted, via the network transceiver 238 as part of or with acontent item request sent to the content selection system. In someinstances, the several sets of values may be stored in the data storagedevice 232 for a predetermined period of time and deleted, erased,overwritten, or otherwise disposed of after the predetermined period oftime (e.g., sets of values may read and stored every 1 minute for a 5minute period, when the 6th minute elapses, the first set of values maybe overwritten with the set of values from the 6th minute). In otherexamples, the set of values may not be stored, but may be read andtransmitted when a content item request is sent.

In some implementations, the code, such as a JavaScript® script, may beincluded or embedded as part of a selected and served content item. Suchcode may be used to receive values for sensor data and response data todetermine one or more predictive models. Accordingly, the code may beincluded in a selected and served content item, that, when executed bythe mobile device 200, will read and transmit the values from the one ormore sensors via the network transceiver 238 to a remote device, such asthe content selection system 108 of FIG. 1. In addition, the code mayreturn response data indicative of whether the content item was selected(such as for CTR calculation), whether a conversion occurred (such asfor CVR calculation), and/or other performance metrics. Thus, the codeincluded with the selected and served content item may be used tocollect sets of values for sensor data and response data for contentitems that are selected and served. The sets of values for sensor dataand response data may be used to generate and/or update one or morepredictive models, as will be described in greater detail below.

FIG. 3 is a block diagram of an example portion of the content selectionsystem 108 for selecting and serving content items. In the presentexample, the content selection system 108 may include a database 310, acontent item selection module 320, and a predictive model module 330.The database 310 may store data for and/or provide data to the contentitem selection module 320 and/or the predictive model module 330. Thedatabase 310 may include a static storage device, such as ROM, solidstate drive (SSD), flash memory (e.g., EEPROM, EPROM, etc.), magneticdisc, optical disc, etc., a plurality of static storage devices, a cloudstorage system, a server, and/or any other electronic device capable ofstoring and providing data. The data stored in the database 310 mayinclude data to effect presentation of the content item, CTR values, CVRvalues, sensor data from one or more sensors and/or from one or moremobile devices, historical sensor data, sequences of served contentitems, impression counts, content item slot positions, numbers ofcontent items in a content item slot, winning bids, selection criteria,format types, keywords, languages, times of a day, days of a week, daysof a month, days of a year, and/or any other data.

In some instances, the data may be related to a parameter of a selectedand served content item. For example, data for content item slotpositions may be related to what positions in a content item slot aparticular content item has been shown. Such data may be used to filterresponse data across a number of content items such that the contentitem slot position is the same for the response data for each contentitem. Accordingly, the response data used to determine a predictivemodel, as will be described below, may filter out aspects that mayaffect the response data. For example, a content item shown in a topposition of a content item slot may be selected more often than acontent item shown in a lower position of a content item slot.Accordingly, the response data may be filtered such that a firstresponse for a content item shown in the top position may be used withother responses for the content item shown in the top position.

In the present example, database 310 includes one or more values ofsensor data from one or more sensors and/or from one or more mobiledevices. In some instances, the values of sensor data for a mobiledevice may be stored as a set of values. The sensor data for a mobiledevice may include values received from one or more of a gyroscope,accelerometer, magnetometer, barometer, proximity sensor, temperaturesensor, light sensor, humidity sensor, acoustic sensor, and/or any othersensor of a mobile device, such as mobile device 200. The sensor datamay be representative of a device state or ambient condition when thesensor data is taken.

In some instances, the sensor data may be associated with dataindicating whether a content item has been clicked on or not, such asresponse data. The response data may be any form of response data thatmay be useful for determining a predictive model. For example, theresponse data may be data indicating whether a content item was clicked(e.g, a 1 if the content item was clicked and a 0 if not), whether aconversion occurred (e.g., a 1 if the selection and serving of thecontent item resulted in a sale and a 0 if not), etc. Thus, the database310 may include a set of values of sensor data and a set of responsedata associated with the set of values of sensor data. As will bediscussed in greater detail below, such response data and sets of valuesof sensor data may be used in determining and/or using a predictivemodel.

The content item selection module 320 may be in communication with thenetwork 106 and may be configured to select one or more content items tobe served with a resource and/or an application. In some instances, thecontent item selection module 320 may perform an auction to select theone or more content items. For example, the content item selectionmodule 320 may select one or more content items of one or morethird-party content providers based, at least in part, on output fromthe predictive model module 230. The selection of the one or morecontent items may be further based on a bid of one or more third-partycontent providers for the one or more content items. Although thepredictive model module 330 is shown separate from the content itemselection module 320, in some implementations, the predictive modelmodule 330 may be part of the content item selection module 320.

As will be described in greater detail below, the predictive modelmodule 330 may output one or more values that may be used by the contentitem selection module 320 to select a content item based on a predictivemodel. For instance, the predictive model may be generated and/orupdated using historical sensor data associated with historical responsedata, which may be stored in database 310. Such a predictive model maybe trained via machine learning algorithms. The predictive model mayoutput a predicted response data value (e.g., pCTR, pCVR, etc.) whenprovided with values of sensor data as an input to the predictive model.For instance, a particular content item may have a higher historical CTRvalue when values of sensor data indicates a series of accelerations andincreases in ambient air pressure (e.g., a content item related to ametro or subway may be clicked on more often when sensor data indicatesthat a mobile device is currently on the metro or subway and/or hashistorically been on the metro or subway). The predictive model may begenerated and/or updated based on a number of historical sensor datasets and historical response data (e.g., whether the content item wasclicked on) to provide an output value indicating that the content itemhas a higher predicted CTR value when subsequent sensor data from amobile device indicates a series of accelerations and increases inambient air pressure. Of course other implementations and/or outputsusing the predictive model may be provided.

FIG. 4 is a flow diagram of an example method 400 for aggregating valuesof sensor data from one or more mobile devices. A request for a contentitem may be received (block 402) by a content selection system, such ascontent selection system 108 of FIG. 1. The content item request may bereceived from a resource server, a mobile device, a user device, and/orotherwise. The request for a content item may include parameters such asa search query string, a keyword, a language, a time of a day, a day ofa week, a day of a month, a day of a year, a resource domain, a browsertype, an operating system type, a content item slot position, a numberof content items to be presented in a content item slot, and/or anyother parameter that may be associated with a request for a contentitem. Such parameters may be used in the selection and serving of acontent item by the content selection system.

Data to effect presentation of a selected content item and code totransmit sensor data may be transmitted (block 404) in response to therequest for a content item. As noted above, in some implementations,code, such as a JavaScript® script, may be included or embedded as partof a selected and served content item. In other implementations, thescript may be sent separately from the data to effect presentation ofthe content item. Such code may be used to read and transmit values forsensor data and response data when a content item is selected and servedto a mobile device. For an example mobile device, the data to effectpresentation of the selected content item and the code may betransmitted via a voice and/or data mobile device communication networkoperating under the standards of GSM, EDGE, GPRS, CDMA, UMTS, WCDMA,HSPA, HSPA+, LTE, and/or other mobile device network standards. In someimplementations, the data to effect presentation of the selected contentitem and the code may be transmitted via WiFi™, Bluetooth®, NFC, and/orotherwise.

Sensor data may be received and stored by the content selection system(block 406). When the code transmitted with the selected and servedcontent item is executed by a mobile device, the output of one or moresensors of the mobile device may be read and transmitted to the contentselection system. For example, values for an accelerometer and agyroscope for the three axes of the mobile device may be read andtransmitted when the code is executed by a processor of the mobiledevice. The set of values of sensor data may be transmitted as separatediscrete values or as a set of values (e.g., an array, an encoded stringvalue, etc.). The received set of values of sensor data may be stored,such as in the database 310 of FIG. 3.

Response data may also be received by the content selection system(block 408). For example, the code transmitted with the selected andserved content item may be used to determine whether the served contentitem is selected, whether a conversion occurs, and/or when any otherevent is performed. For example, a JavaScript® script may transmit afirst value, e.g., a 1, if the served content item is selected by a userof the mobile device and a second value, e.g., a 0, if the servedcontent item is not selected. The script may determine that the contentitem is not selected if a predetermined period of time elapses without aselection and/or if the content item is no long displayed (e.g., by auser navigating away from a webpage, by terminating the execution of anapplication, etc.). The script may transmit the response data to thecontent selection system by causing a processor of the mobile device totransmit the response data via a network transceiver.

The received set of values of sensor data may be associated with thereceived response data (block 410), such as by the content selectionsystem. For example, the received set of values of sensor data and thereceived response data may be stored together and associated in a devicestate history, a log, and/or other data file in a database and/orotherwise. In some implementations, the received set of values of sensordata and the received response data may be further associated with theserved content item (e.g., by an identifier of the served content itemor otherwise), with a category or group of the served content item(e.g., by an identifier of a category or group of the served contentitem or otherwise), with similar sets of sensor data having similarvalues of other mobile devices (e.g., by an identifier for an aggregategrouping of similar sensor data), and/or with otherwise. The receivedset of values of sensor data and the received response data may be usedin the determining of a predictive model.

In some instances, the sensor data may be bucketized (e.g., utilizing avalue representative of a range of discrete values). For example, thevalues of degrees of rotation about an axis may be bucketized into lessthen 45 degrees, between 45 and 90 degrees, inclusive, between 90 and135 degrees, inclusive, between 135 and 180 degrees, less then −45degrees, between −45 and −90 degrees, inclusive, between −90 and −135degrees, inclusive, and between −135 and −180 degrees, inclusive. Ofcourse the foregoing is merely an example and other ranges may be usedand/or other levels of abstraction may be implemented (e.g., verticalorientation, horizontal orientation, tilted, etc.). Moreover, in someinstances, machine learning algorithms may be used to determine theranges for the various sensor data for abstraction. For example, it maybe determined that accelerations over 15 meters per second may not occurvery often. Accordingly, an end range or bucket for acceleration may bedetermined for sensor data for accelerations greater than 15 meters persecond. Smaller range buckets may be determined to differentiate smalleraccelerations, such as buckets for acceleration values of exactly 0,between 0 and 0.1, inclusive, between 0.1 and 0.5, inclusive, between0.5 and 0.75, inclusive, etc. In still further implementations, a userof a mobile device may indicate the ambient condition that occurred whenthe sensor data was taken (e.g., via an application or otherwise). Forexample, a set of sensor data may be read and transmitted and the usermay indicate an ambient condition corresponding to the data, such as“running,” “jogging,” “driving,” etc. Accordingly, such tagged sensordata having values may be associated with other similar sensor data. Inaddition, subsequent sets of sensor data may be categorized as belongingto one of the high level abstractions or ambient conditions and the highlevel abstraction or ambient condition may be used as an input into thepredictive model in the selection and serving of a content item.

FIG. 5 is a flow diagram of an example method 500 for determining apredictive model to output a value based on sensor data. The method 500may include receiving a set of sensor data and associated response data(block 502). The response data may be any form of response data that maybe useful for determining a predictive model. For example, the responsedata may be data indicating whether a content item was clicked (e.g, a 1if the content item was clicked and a 0 if not), whether a conversionoccurred (e.g., a 1 if the selection and serving of the content itemresulted in a sale and a 0 if not), etc.

The set of sensor data may include several sets of sensor data receivedfrom several different mobile devices. The sets of sensor data may eachinclude a set of values representing values outputted from sensors fromthe respective mobile devices. For example, the set of values mayinclude those outputted by a gyroscope, an accelerometer, amagnetometer, a barometer, a proximity sensor, a temperature sensor, alight sensor, a humidity sensor, an acoustic sensor, and/or othersensors. In some instances, the values may be bucketized values, highlevel abstractions or ambient conditions, and/or otherwise as describedabove. In some implementations, the received set of sensor data and theresponse data may be associated with a content item of a set of contentitems.

Using the set of sensor data and the response data, a predictive modelmay be determined (block 504). For example, CTR values, as determined bythe response data, for a certain content item may be determined to belower when certain values of sensor data are present while CTR valuesfor the same content item may be higher when other values of sensor dataare present. Thus, the determined predictive model may output a higherpredicted CTR value when the certain values of sensor data are presentfor that content item, and lower predicted CTR values when the othervalues of sensor data are present. By way of example, a content itemrelated to reading may have a higher CTR when the sensor data includesvalues that indicate little or no acceleration for the three axes, thedegrees of rotation indicate the device is laying flat, and an object isproximate to the screen of the device (e.g., when a user is reading onthe device). In other instances, other content items may have higherCTRs when the sensor data includes values that indicate other ambientconditions (e.g., content items for restaurants, gas stations, hotels,attractions, etc. may have higher CTRs for sensor data that has valuesindicating a moderate acceleration or deceleration, a verticalorientation, and an object proximate to the screen, such as may occurwhen a passenger in a vehicle is reading the device). As will bediscussed below, such a determined predictive model may be used tooutput predicted CTR, CVR, or other values for a content item that maybe used in the selection and serving of a content item from severalcontent items.

In some instances, the predictive model may be determined for a group orcategory of content items. For example, the predictive model may bedetermined based on sets of sensor data and response data associatedwith a group or category of content items. Such a predictive model maybe used to output predicted CTR, CVR, or other values for groups orcategories of content items that may be used in the selection of a groupor category of content items and, subsequently, to select and serve acontent item from several content items of the group or category.

FIG. 6 depicts a flow diagram of an example of a method 600 forselecting content items using a predictive model. The method 600 mayinclude receiving a first content item request and a first set of sensordata (block 602). The first content item request may be received by adata processor of a content selection system from a resource server, auser device, a mobile device, and/or otherwise. The first set of sensordata may include several values from respective sensors of a mobiledevice, such as the sensors of mobile device 200. The several values mayidentify a first ambient condition of the mobile device. For instance, ahigh acceleration and high ambient air pressure may identify an ambientcondition that occurs when riding the metro or subway. Of course, othervalues may identify other ambient conditions, such as indicating anambient condition of a user reading the mobile device when the sensordata includes values that indicate little or no acceleration for thethree axes, the degrees of rotation indicate the device is laying flat,and an object is proximate to the screen of the device. Still othervalues may identify other ambient conditions.

A first content item may be selected based, at least in part, on apredictive model (block 606). The predictive model may output a value,such as a pCTR, pCVR, etc., based on the received first set of sensordata having a first set of values. The predictive model may bedetermined and/or trained in accordance with method 500 described hereinand/or otherwise. In some instances, the predictive model may beselected from several predictive models, such as a predictive model fora specific content item, a group or category of content items, and/orotherwise. For each content item, an output value from the predictivemodel may be determined. For instance, the output value may be a pCTRvalue, a pCVR value, and/or other value for the content item based onthe received sensor data having a set of values. In someimplementations, the content item with the highest output value, asdetermined by the predictive model, may be selected. In some otherimplementations, the output values from the predictive model for eachcontent item may be used in further determinations for which contentitem should be selected (e.g., in an auction of content items, etc.).For example, a content provider may have a higher bid value for acontent item for certain values of the sensor data (e.g., thoseindicating a certain ambient condition) and a lower bid value for othervalues of the sensor data. Accordingly, the third-party content providermay provide various bid amounts for various values of the sensor dataand/or for various identified ambient conditions.

Data to effect presentation of the first selected content item may beoutputted (block 608) such that the first selected content item may bepresented on a display of the mobile device with a resource, such as arequested mobile webpage and/or a mobile application. In someimplementations, the method 600 may end with the presentation of thefirst selected content item (as noted by the dashed line).

In other implementations, the method 600 may continue to update and/orselect a second content item. Response data associated with the selectedfirst content item may be received (block 608). For example, the codetransmitted with the selected and served content item may be used todetermine whether the served content item is selected, whether aconversion occurs, and/or when any other event is performed. Forexample, a JavaScript® script may transmit a first value, e.g., a 1, ifthe served content item is selected by a user of the mobile device and asecond value, e.g., a 0, if the served content item is not selected. Thescript may determine that the content item is not selected if apredetermined period of time elapses without a selection and/or if thecontent item is no long displayed (e.g., by a user navigating away froma webpage, by terminating the execution of an application, etc.). Thescript may transmit the response data to the content selection system,such as content selection system 108, by causing a processor of themobile device to transmit the response data via a network transceiver ofthe mobile device. The received response data may be associated with thefirst sensor data and the selected first content item and stored in adatabase, such as database 310.

The predictive model may be updated using the received response data(block 610). For example, the predictive model may be updated via thedetermining and/or training of the method 500 described herein and/orotherwise. The received response data may be associated with the firstsensor data and combined with a number of historical sensor data setsand historical response data, such as that stored in a database, to beused in updating the predictive model. Accordingly, the predictive modelmay be updated for subsequent sensor data and response data. Thepredictive model may be a predictive model for a specific content item,a group or category of content items, and/or otherwise. In someimplementations, the method 600 may end with the update of thepredictive model (as noted by the dashed line).

In some implementations, the method 600 may continue and includereceiving a second content item request and a second set of sensor data(block 612). The second content item request may be received by a dataprocessor of a content selection system from a resource server, a userdevice, a mobile device, and/or otherwise. The second set of sensor datamay include several values from respective sensors of a mobile device,such as the sensors of mobile device 200. In some instances, the mobiledevice may be the same mobile device from which the first set of sensordata was received or, in other instances, the mobile device may be adifferent mobile device The several values may identify a second ambientcondition of the mobile device. In some instances, the second ambientcondition may be the same as the first ambient condition or may be adifferent ambient condition.

A second content item may be selected based, at least in part, on theupdated predictive model (block 614). The updated predictive model mayoutput a value, such as a pCTR, pCVR, etc., based on the received secondset of sensor data having a second set of values. For each content item,an output value from the updated predictive model may be determined. Insome implementations, the content item with the highest output value, asdetermined by the updated predictive model, may be selected. In someother implementations, the output values from the updated predictivemodel for each content item may be used in further determinations forwhich content item should be selected (e.g., in an auction of contentitems, etc.). For example, a content provider may have a higher bidvalue for a content item for certain values of the sensor data (e.g.,those indicating a certain ambient condition) and a lower bid value forother values of the sensor data. Accordingly, the third-party contentprovider may provide various bid amounts for various values of thesensor data and/or for various identified ambient conditions.

Data to effect presentation of the second selected content item may beoutputted (block 616) such that the second selected content item may bepresented on a display of the mobile device with a resource, such as arequested mobile webpage and/or a mobile application.

FIG. 7 depicts a flow diagram of an example of a method 700 forselecting a content item using a predictive model and historical sensordata associated with a device identifier. The method 700 may includereceiving a content item request, a set of sensor data, and a deviceidentifier (block 702). The content item request may be received by adata processor of a content selection system from a resource server, auser device, a mobile device, and/or otherwise. The set of sensor datamay include several values from respective sensors of a mobile device,such as the sensors of mobile device 200. The several values mayidentify an ambient condition of the mobile device. For instance, a highacceleration and high ambient air pressure may identify an ambientcondition that occurs when riding the metro or subway. Of course, othervalues may identify other ambient conditions. The device identifier maybe a randomized number associated with the mobile device to identify themobile device during subsequent requests for resources and/or contentitems.

Historical sensor data associated with the received device identifiermay be received (block 704). The historical sensor data may be storedand/or received from a device state history associated with the deviceidentifier of a database, such as database 310 of FIG. 3. The historicalsensor data of the device state history may be temporarily stored for apredetermined period of time (e.g., sensor data for the past minute,past 5 minutes, past 10 minutes, past 30 minutes, past hours, past 2hours, past 4 hours, past 6 hours, past 12 hours, past 24 hours, etc.)such that the historical sensor data is erased after a pre-determinedperiod of time (e.g., after 1 minute, 5 minutes, 10 minutes, 30 minutes,1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, etc.). As notedabove, the historical sensor data may be periodically received by acontent selection system and/or sent by the mobile device and stored inthe device state history or otherwise in a database of the contentselection system. In some other instances, the historical sensor datamay result after storing prior received sensor data from prior receivedcontent item requests. Once the predetermined period of time haselapsed, such historical sensor data from the device state history maybe deleted, overwritten, and/or otherwise removed from the database suchthat the historical sensor data for which the predetermined period haselapsed is no longer available. In some instances, the historical sensordata may be stored chronologically, such as in a time series.

A content item may be selected based, at least in part, on a predictivemodel (block 706). The predictive model may output a value, such as apCTR, pCVR, etc., based on the received set of sensor data having a setof values and the received historical sensor data having one or moresets of values. The predictive model may be determined and/or trained inaccordance with method 500 described herein and/or otherwise. In someinstances, the predictive model may be selected from several predictivemodels, such as a predictive model for a specific content item, a groupor category of content items, and/or otherwise. For each content item,an output value from the predictive model may be determined. Forinstance, the output value may be a pCTR value, a pCVR value, and/orother value for the content item based on the received sensor datahaving a set of values and the received historical sensor data havingone or more sets of values. In some implementations, the content itemwith the highest output value, as determined by the predictive model,may be selected. In some other implementations, the output values fromthe predictive model for each content item may be used in furtherdeterminations for which content item should be selected (e.g., in anauction of content items, etc.). For example, a content provider mayhave a higher bid value for a content item for certain values of thesensor data (e.g., those indicating a certain ambient condition) and alower bid value for other values of the sensor data. Accordingly, thethird-party content provider may provide various bid amounts for variousvalues of the sensor data and/or for various identified ambientconditions.

In some instances, the received sensor data and the historical sensordata may be analyzed to determine whether sets of values recur (e.g.,indicating a repeating ambient condition, such as riding the metro,jogging, running, etc.). The recurring sets of values may be selectedand input into the predictive model. Thus, a content item may beselected based on the recurring sets of values. For example, for sets ofsensor data and historical sensor data that indicate a mobile device isused while jogging, the output of the predictive model may be used toselect a content item associated with jogging, even if the currentsensor data does not have values indicating the ambient condition ofjogging.

Data to effect presentation of the selected content item may beoutputted (block 708) such that the selected content item may bepresented on a display of the mobile device with a resource, such as arequested mobile webpage and/or a mobile application.

FIG. 8 is a block diagram of a computer system 800 that can be used toimplement the resource server 104, user device 110, content selectionsystem 108, third-party content server 102, etc. The computing system800 includes a bus 805 or other communication component forcommunicating information and a processor 810 or processing modulecoupled to the bus 805 for processing information. The computing system800 can also include one or more processors 810 or processing modulescoupled to the bus for processing information. The computing system 800also includes main memory 815, such as a RAM or other dynamic storagedevice, coupled to the bus 805 for storing information, and instructionsto be executed by the processor 810. Main memory 815 can also be usedfor storing position information, temporary variables, or otherintermediate information during execution of instructions by theprocessor 810. The computing system 800 may further include a ROM 820 orother static storage device coupled to the bus 805 for storing staticinformation and instructions for the processor 810. A storage device825, such as a solid state device, magnetic disk or optical disk, iscoupled to the bus 805 for persistently storing information andinstructions. Computing device 800 may include, but is not limited to,digital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, cellulartelephones, smart phones, mobile computing devices (e.g., a notepad,e-reader, etc.) etc.

The computing system 800 may be coupled via the bus 805 to a display835, such as a Liquid Crystal Display (LCD), Thin-Film-Transistor LCD(TFT), an Organic Light Emitting Diode (OLED) display, LED display,Electronic Paper display, Plasma Display Panel (PDP), and/or otherdisplay, etc., for displaying information to a user. An input device830, such as a keyboard including alphanumeric and other keys, may becoupled to the bus 805 for communicating information and commandselections to the processor 810. In another implementation, the inputdevice 830 may be integrated with the display 835, such as in a touchscreen display. The input device 830 can include a cursor control, suchas a mouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 810 andfor controlling cursor movement on the display 835.

According to various implementations, the processes and/or methodsdescribed herein can be implemented by the computing system 800 inresponse to the processor 810 executing an arrangement of instructionscontained in main memory 815. Such instructions can be read into mainmemory 815 from another computer-readable medium, such as the storagedevice 825. Execution of the arrangement of instructions contained inmain memory 815 causes the computing system 800 to perform theillustrative processes and/or method steps described herein. One or moreprocessors in a multi-processing arrangement may also be employed toexecute the instructions contained in main memory 815. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to effect illustrativeimplementations. Thus, implementations are not limited to any specificcombination of hardware circuitry and software.

Although an example computing system 800 has been described in FIG. 8,implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

Implementations of the subject matter and the operations described inthis specification can be implemented in digital electronic circuitry,or in computer software embodied on a tangible medium, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.The subject matter described in this specification can be implemented asone or more computer programs, i.e., one or more modules of computerprogram instructions, encoded on one or more computer storage media forexecution by, or to control the operation of, data processing apparatus.Alternatively or in addition, the program instructions can be encoded onan artificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate components or media (e.g., multiple CDs, disks, or otherstorage devices). Accordingly, the computer storage medium is bothtangible and non-transitory.

The operations described in this specification can be performed by adata processing apparatus on data stored on one or morecomputer-readable storage devices or received from other sources.

The term “data processing apparatus” or “computing device” or“processing circuit” encompasses all kinds of apparatus, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, a system on a chip, or multiple ones, a portionof a programmed processor, or combinations of the foregoing. Theapparatus can include special purpose logic circuitry, e.g., an FPGA oran ASIC. The apparatus can also include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVDdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD monitor,for displaying information to the user and a keyboard and a pointingdevice, e.g., a mouse or a trackball, by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features specific to particularembodiments. Certain features described in this specification in thecontext of separate embodiments can also be implemented in combinationin a single implementation. Conversely, various features described inthe context of a single implementation can also be implemented inmultiple embodiments separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated in a single software product or packaged intomultiple software products embodied on tangible media.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain embodiments, multitasking and parallel processingmay be advantageous.

The claims should not be read as limited to the described order orelements unless stated to that effect. It should be understood thatvarious changes in form and detail may be made by one of ordinary skillin the art without departing from the spirit and scope of the appendedclaims. All embodiments that come within the spirit and scope of thefollowing claims and equivalents thereto are claimed.

What is claimed is:
 1. A method comprising: receiving, at one or moredata processors, a first content item request and a first plurality ofvalues received from a respective plurality of sensors of a mobiledevice, the first plurality of values combined with the first contentitem request in a first URL string, the first plurality of valuesindicating a first ambient condition of the mobile device; selecting,using one or more data processors, a first content item based on apredictive model for selecting content items and the first plurality ofvalues, the predictive model selecting the first content item from aplurality of candidate content items using associations between i)response data received from the mobile devices in response to theplurality of candidate content items presented on the mobile devices ii)values corresponding to sensors of mobile devices that are included inrequests for content responsive to which the plurality of candidatecontent items are transmitted to the mobile devices, the predictivemodel maintaining, for each content item of the plurality of candidatecontent items, a performance metric based on the response datacorresponding to the content item; outputting data to effectpresentation of the selected first content item; receiving, at one ormore data processors, first response data transmitted by the mobiledevice in response to presentation of the selected first content item atthe mobile device associated with the selected first content item;associating, at the one or more data processors, the first response datacorresponding to the selected first content item to the first pluralityof values included in the first content item request corresponding tothe selected first content item; updating the predictive model byassociating the first response data corresponding to the selected firstcontent item and the first plurality of values included in the firstcontent item request corresponding to the selected first content item;receiving, at one or more data processors, a second content item requestand a second plurality of values received from a respective plurality ofsensors of the mobile device, the second plurality of values combinedwith the second content item request in a second URL string, theplurality of values indicating a second ambient condition of the mobiledevice; and selecting, using one or more data processors, a secondcontent item from the plurality of candidate content items based on theupdated predictive model and the second plurality of values.
 2. Themethod of claim 1, wherein the first ambient condition and the secondambient condition are different; and wherein the first selected firstcontent item and the selected second content item are different.
 3. Themethod of claim 1, wherein the first plurality of values received fromthe respective plurality of sensors is representative of at least one ofan acceleration of the mobile device, a direction of gravity relative tothe mobile device, a rate of rotation of the mobile device, a degree ofrotation of the mobile device, an ambient geomagnetic field, a proximityof an object relative to the mobile device, an ambient light level, anambient temperature, an ambient humidity, or an ambient air pressure. 4.The method of claim 1, wherein the first content item request isassociated with a mobile webpage.
 5. The method of claim 1, wherein thefirst content item request is associated with a mobile application. 6.The method of claim 1, wherein the predictive model is associated with acontent item.
 7. The method of claim 1, wherein the predictive model isassociated with a category of content items.
 8. The method of claim 1further comprising: storing the received first plurality of valuesreceived from the respective plurality of sensors of the mobile devicein a device state history associated with the mobile device, theselection of the first content item is further based on the device statehistory.
 9. A system comprising: one or more data processors; and astorage device storing instructions that, when executed by the one ormore data processors, cause the one or more data processors to: receivea first content item request and a first plurality of values receivedfrom a respective plurality of sensors of a mobile device, the firstplurality of values combined with the first content item request in afirst URL string, the first plurality of values indicating a firstambient condition of the mobile device, select a first content itembased on a predictive model for selecting content items and the firstplurality of values, the predictive model selecting the first contentitem from a plurality of candidate content items using associationsbetween i) response data received from the mobile devices in response tothe plurality of candidate content items presented on the mobile devicesii) values corresponding to sensors of mobile devices that are includedin requests for content responsive to which the plurality of candidatecontent items are transmitted to the mobile devices, the predictivemodel maintaining, for each content item of the plurality of candidatecontent items, a performance metric based on the response datacorresponding to the content item, output data to effect presentation ofthe selected first content item, receive first response data transmittedby the mobile device in response to presentation of the selected firstcontent item at the mobile device associated with the selected firstcontent item, associate the first response data corresponding to theselected first content item to the first plurality of values included inthe first content item request corresponding to the selected firstcontent item, update the predictive model by associating the firstresponse data corresponding to the selected first content item and thefirst plurality of values included in the first content item requestcorresponding to the selected first content item, receive a secondcontent item request and a second plurality of values received from arespective plurality of sensors of the mobile device, the secondplurality of values combined with the second content item request in asecond URL string, the plurality of values indicating a second ambientcondition of the mobile device, select a second content item from theplurality of candidate content items based on the updated predictivemodel and the second plurality of values, and output data to effectpresentation of the selected second content item.
 10. The system ofclaim 9, wherein the first ambient condition and the second ambientcondition are different.
 11. The system of claim 9, wherein the firstplurality of values received from the respective plurality of sensors isrepresentative of at least one of an acceleration of the mobile device,a direction of gravity relative to the mobile device, a rate of rotationof the mobile device, a degree of rotation of the mobile device, anambient geomagnetic field, a proximity of an object relative to themobile device, an ambient light level, an ambient temperature, anambient humidity, or an ambient air pressure.
 12. The system of claim 9,wherein the first content item request is associated with a mobilewebpage.
 13. The system of claim 9, wherein the first content itemrequest is associated with a mobile application.
 14. The system of claim9, wherein the first plurality of values from the mobile device isreceived as part of a first content item request from an applicationexecuted on the mobile device.
 15. The system of claim 9 furthercomprising: storing the received first plurality of values received fromthe respective plurality of sensors of the mobile device in a devicestate history associated with the mobile device, the selection of thefirst content item is further based on the device state history.
 16. Acomputer-readable storage device storing instructions that, whenexecuted by one or more data processors, cause the one or more dataprocessors to perform operations comprising: receiving a first contentitem request and a first plurality of values received from a respectiveplurality of sensors of a mobile device, the first plurality of valuesindicating a first ambient condition of the mobile device; selecting afirst content item based on a predictive model for selecting contentitems and the first plurality of values, the predictive model selectingthe first content item from a plurality of candidate content items usingassociations between i) response data received from the mobile devicesin response to the plurality of candidate content items presented on themobile devices ii) values corresponding to sensors of mobile devicesthat are included in requests for content responsive to which theplurality of candidate content items are transmitted to the mobiledevices, the predictive model maintaining, for each content item of theplurality of candidate content items, a performance metric based on theresponse data corresponding to the content item; outputting data toeffect presentation of the selected first content item; receiving firstresponse data transmitted by the mobile device in response topresentation of the selected first content item at the mobile deviceassociated with the selected first content item; associating, at the oneor more data processors, the first response data corresponding to theselected first content item to the first plurality of values included inthe first content item request corresponding to the selected firstcontent item; updating the predictive model by associating the firstresponse data corresponding to the selected first content item and thefirst plurality of values included in the first content item requestcorresponding to the selected first content item; receiving a secondcontent item request and a second plurality of values received from arespective plurality of sensors of the mobile device, the plurality ofvalues indicating a second ambient condition of the mobile device; andselecting a second content item from the plurality of candidate contentitems based on the updated predictive model and the second plurality ofvalues.
 17. The computer-readable storage device of claim 16, whereinthe first plurality of values received from the respective plurality ofsensors is representative of at least one of an acceleration of themobile device, a direction of gravity relative to the mobile device, arate of rotation of the mobile device, a degree of rotation of themobile device, an ambient geomagnetic field, a proximity of an objectrelative to the mobile device, an ambient light level, an ambienttemperature, an ambient humidity, or an ambient air pressure.
 18. Thecomputer-readable storage device of claim 16, wherein the firstplurality of values from the mobile device is received as part of afirst content item request from an application executed on the mobiledevice.
 19. The computer-readable storage device of claim 16, whereinthe first plurality of values from the mobile device is received as partof a first content item request for a mobile webpage.
 20. Thecomputer-readable storage device of claim 16, wherein the predictivemodel is associated with a category of content items.